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Last updated on 2022-05-13.

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Alexandre Henrique S. Dias

I’m a Full-Time Data Scientist and a MSc student in Electrical and Computer Engineering at UFRN. My research is focused on social network analysis, graph theory, and Natural Language Processing. Additionaly, the main programming languages I use are Python, R, C++, and SQL. Besides, my favorite ML FrameWorks are Scikit-Learn and TensorFlow. Lastly, I also have skills in MLOps using GKE, Kubeflow, Kubernetes, and Docker.

Industry Experience

Data Scientist

Americanas S.A.

São Paulo, SP

Present - 2021

  • Responsible for building ML models using: Python, Scikit-Learn, and Tensorflow. Apply ML to a wide range of topics, such as Complex Network Analysis, Social Networks, NLP, and HR Analytics.
  • Create ML pipelines using KubeFlow Pipelines from Google Cloud AI Platform, and participate in the design of CI/CD operations of ML models.

Data Scientist

Looqbox

São Paulo, SP

2021 - 2019

  • Development of BI reports and dashboards using R, Python, and SQL.
  • Maintainer of the Looqbox R Package used to build R objects and data structures compatible with the Looqbox Application.

Education

M. Sc., Electrical and Computer Engineering

UFRN - Federal University of Rio Grande do Norte

Natal, RN

Present - 2021

  • Research in complex network analysis, social networks, graph theory, and NLP.
  • Tools: python, networkX, gephi, TensorFlow, WandB, Git.

MITx Micromaster Program in Statistics and Data Science

MITx on EdX

EdX

2022 - 2020

  • The MITx MicroMaster Program in Statistics and Data Science covers the fundamentals of data science, statistics, and machine learning.

B. Sc., Computer Engineering

UFRN - Federal University of Rio Grande do Norte

Natal, RN

2019 - 2018

  • Researcher and member of the Modeling and Scientific Data Analysis team.

B. Sc., Sciences & Technology

UFRN - Federal University of Rio Grande do Norte

Natal, RN

2017 - 2015

  • Linear Algebra and Analytical Geometry Teacher Assistant.
  • Calculus II Teacher Assistant.

Certificates & Courses

MicroMasters in Statistics and Data Science

MITx on EdX

N/A

2022 - 2020

  • 6.431x: Probability - The Science of Uncertainty and Data.
  • 18.6501x: Fundamentals of Statistics.
  • 6.86x: Machine Learning with Python - From Linear Models to Deep Learning.
  • 14.310x/Fx: Data Analysis in Social Science.
  • DS.CFx: Capstone Exam for Statistics and Data Science.

MLOps (Machine Learning Operations) Fundamentals

Coursera

N/A

2021

DataCamp completed tracks

DataCamp

N/A

2019 - 2018

  • Data Scientist with Python.
  • Data Analyst with Python.
  • Data Manipulation with Python.
  • Machine Learning with Python.
  • Importing & Cleaning Data with Python.
  • Python Programming.
  • Python Programmer.

Academic Publications

Paper published in the 2019 II Workshop on Metrology for Industry 4.0 and IoT (MetroInd4.0&IoT). Naples, Italy.

Performance Evaluation of an Edge OBD-II Device for Industry 4.0

Institute of Electrical and Electronics Engineers

IEEE

2019

  • Performance evaluation of an Edge OBD-II device that collects data from vehicles in an autonomous way in order to provide customer feedback and tracking

Research Experience

Undergraduate Researcher

Digital Metropolis Institute

UFRN

2019 - 2018

  • Developed a traffic monitoring system using image recognition techniques.

Undergraduate Researcher

Department of Informatics and Applied Mathematics

UFRN

2017 - 2016

  • Developed an interactive theorem prover based on Linear Logic using the Maude programming language.

Selected Data Science Writing

I enjoy reading about productivity, lifestyle, data science/AI, and statistics.

Dimensionality Reduction with Factor Analysis on Student Performance Data

N/A

Geek Culture

2021

  • A dimensionality reduction technique with interpretable outputs.

Stop Using the Elbow Method

N/A

Geek Culture

2021

  • Silhouette Analysis: A more precise approach to finding the optimal number of clusters using K-Means.

Scikit-Learn 1.0 - A true milestone

N/A

Medium

2021

  • An overview of the design principles of Scikit-Learn and how the famous ML library became so popular.

The Expectation-Maximization (EM) Algorithm

N/A

B2W Engineering

2021

  • Understanding the motivations and how the EM Algorithm works.

A mathematical derivation of the Law of Total Variance

N/A

The Startup

2020

  • Understanding what is and when to apply the Law of Total Variance.

Clustering with K-means: simple yet powerful

N/A

Medium

2019

  • Explain what is Cluster Analysis, and how the K-means algorithm work providing its pros and cons.

An introduction to Linear Regression

N/A

Medium

2019

  • Explain all assumptions behind Linear Regression, how to measure its performance, and how to implement it in Python.